Number Of Pickers Research Articles

Workload balancing and scheduling in picking tower systems considering different storage strategies

Picking tower systems are particularly suitable in the e-commerce distribution field. They permit to stock and pick a wide variety of items by maximizing storage capacity and space utilization. Such types of systems are comparable to zone picking solutions, since pickers work on one floor during the same working shift. However, differences also exist since, for example, once the picking tower system is designed, it is not possible to improve or reduce the area of each floor. Thus, a proper workload balancing is required to avoid an efficiency reduction of this system. This paper proposes a mixed integer linear programming mathematical model to assign pickers to floors and jointly schedule the picking list of each of them aiming to minimize the completion time of all orders. The model is applied to a real case study and first managerial insights are derived by investigating how the number of floors, the storage strategy and the number of pickers allowed on each floor influence the workload and the completion time. The results show that, for the considered case, the most effective configuration is the brand-based storage strategy with four picking levels.

Considering pickers’ learning effects in selecting between batch picking and batch-synchronized zone picking for online-to-offline groceries

The performance of two common picking strategies, batch picking (BP) and batch-synchronized zone picking (BSZP), are compared by considering pickers’ learning effects in online-to-offline (O2O) groceries. As we know from previous research, one major advantage of BSZP is that pickers can be more familiar with the item locations because each picker works in a relatively smaller picking area. However, few papers have formulated a quantitative method to verify this advantage. To address this, we use pickers’ learning effects to quantify their familiarity with the item locations, and then compare the picking efficiency in BP and BSZP with learning effects. The optimization models of BP and BSZP are formulated to minimize the total service time, and heuristic methods are proposed to solve these models. The case study is implemented under three order types and three storage assignment policies, i.e. Random, ABC-1, and ABC-2. The results of the study indicate that with learning effects, although BSZP needs a longer travel time than BP, it requires a shorter search time. Which strategy, BP or BSZP, is more efficient mainly depends on the learning rates, storage assignment policies, and pickers’ working days. When the storage assignments are Random and ABC-1, a smaller learning rate can lead BSZP to perform better than BP. When the storage assignment is ABC-2, BSZP performs better than BP. When pickers’ working days are long enough, the learning effect is weakened, and BP outperforms. Awareness of the learning effects is required to choose a better picking strategy, evaluate more accurate picking efficiency and hire an appropriate number of pickers.

Research on congestion rate of classified storage narrow channel picking system for IoT security

The smart mobile Internet of Things (IoT) is a novel paradigm that is rapidly gaining ground in the field of modern wireless telecommunications. Picking operations are the key link in an entire warehousing operation, and congestion problems significantly contribute to managers’ decisions regarding the warehousing layout, the storage strategy, and the number of pickers. In this paper, the discrete Markov method is used to study the problem of picking congestion in a narrow-aisle double picking system with a traditional layout, an S-type picking path and a classified storage strategy. First, congestion models are constructed when the picker picks one item with a picking speed that is equal to the walking speed and when the picker picks multiple items on one picking surface under a picking speed that is very different from the walking speed. A sensitivity analysis involving parameter changes was carried out. Second, the paper compares and analyzes situations in which the picking speed is equal to the walking speed and in which there is a large difference between the picking speed and the walking speed and determines the impact of the picking speed, the number of goods picked on a picking surface, the differentiated classified storage strategy, and the number of picking surfaces on the picking congestion rate. The effectiveness of the mathematical model was verified by a MATLAB simulation, and the exploration and discovery for subsequent research were performed.

A hybrid artificial neural network, genetic algorithm and column generation heuristic for minimizing makespan in manual order picking operations

At an operational level, order picking is the main activity in fulfillment centers. Motivated by and through collaboration with a third party logistic company, this study presents a novel hybrid column generation (CG), genetic algorithm (GA), and artificial neural network (ANN) heuristic for minimizing makespan in manual order picking operations. The results of column generation heuristic is compared against a mixed integer programming model solved by Gurobi, and a parallel simulated annealing and ant colony optimization (PSA-ACO) previously proposed in the literature. Through numerical experiments, the superiority of CG heuristic compared to other methods is shown, and some managerial insights regarding the relationship between makespan optimization, workload balance, picking capacity, and number of pickers in order picking operations is presented.

복수포장대 좁은통로 피킹시스템의 주문묶음 알고리즘

This study proposes a joint order batching and depot selecting model (JOBDS) in narrow-aisle order picking systems (OPSs). The two depots in a hypothetical narrow-aisle OPS distribute the operational burden imposed by inspectidon and packaging. We build an order batching model and develop a mixed integer programming (MIP) algorithm considering the travel distance to each depot’s location for a 48-order and a 72-order case. We also develop large-scale heuristic algorithms: Seed and Saving algorithms. A comparison to order batching model with single depot (OBSD) shows that the proposed algorithms shorten the travel distance on average 4.19 ~ 13.38%. We provide additional simulation studies to analyze total travel time by walk speed, order size, inspection and packaging times, number of pickers, and location of depots. Consistently, travel time in JOBDS is operationally advantageous compared to OBSD.

THE NUMBER OF PICKERS AND STOCK-KEEPING UNIT ARRANGEMENT ON A UNI- DIRECTIONAL PICKING LINE

The order picking process is often the single largest expense in a distribution centre (DC). The DC considered in this paper uses a picking line configuration to perform order picking. The number of pickers in a picking line, and the initial arrangement of stock-keeping units (SKUs), are two important factors that affect the total completion time of the picking lines. In this paper, the picking line configuration is simulated with an agent-based approach to describe the behaviour of an individual picker. The simulation is then used to analyse the effect of the number of pickers and the SKU arrangement. Verification and validation of this model shows that the model represents the real-world picking line to a satisfactory degree. Marginal analysis (MA) was chosen to determine a ‘good’ number of pickers by means of the simulation model. A look-up table is presented to provide decision support for the choice of a ‘good’ number of pickers to improve completion times of the picking line, for the properties of a specific picking line. The initial SKU arrangement on a picking line is shown to be a factor that can affect the level of picker congestion and the total completion time. The greedy ranking and partitioning (GRP) and organ pipe arrangement (OPA) techniques from the literature, as well as the historical SKU arrangements used by the retailer under consideration, were compared with the proposed classroom discipline heuristic (CDH) for SKU arrangement. It was found that the CDH provides an more even spread of SKUs that are picked most frequently, thus decreasing congestion and total completion time. OPSOMMING

Throughput analysis for order picking system with multiple pickers and aisle congestion considerations

Most of previous studies in picker-to-parts warehousing systems investigated only single-picker operations and are therefore adequate to evaluate order picking efficiency by travel distance as aisle congestion never takes place in such systems. In real world applications, the congestion inevitably occurs when a system has multiple pickers working together within the same region. This paper presents an approximation method based on a GI/G/1 closed queueing network by using self-correcting approximation technique algorithm to evaluate the throughput time of an order picking system with multiple pickers and aisle congestion considerations for different routing policies. The results generated by the proposed method are compared and validated via simulation model using eM-plant simulator for different sizes of warehouses. The results indicate that the approximation method appears to be sufficiently accurate for practical purposes. The sensitivity analysis of the throughput time with respect to order sizes, number of pickers and number of aisles are conducted and the performance of different item storage policies are also evaluated using the proposed approximation model.

Development of a framework for pick-and-pass order picking system design

In today’s competitive scenario of increasingly faster deliveries and smaller order sizes, a solution that is being used more frequently is the pick-and-pass Order Picking System (OPS). The purpose of this study is to define a framework for the pick-and-pass system design, by expanding previous literature on this topic. The framework aims at minimising the overall picking costs while respecting the required service level (i.e. order throughput time), and can be easily applied to the selection stage of OPS design. The number of zones and the number of pickers per zone are among the main project data for the framework application. Analytical models are used to estimate the travel distance, and network queuing theory to analyse the mean order throughput. On the basis of the proposed framework, pick-and-pass system performance is examined as a function of two typical design conditions: order size and number of items.

A multinutrient package of iron, vitamin A, and iodine improved the productivity and earnings of women tea pickers in south India.

Tea-picking is a highly skilled activity that is usually performed by women. This study, conducted on the Balanoor Plantations, India, from 1996 to 1998, was successful in empowering 339 women pickers and their families to take iron (60 mg of elemental iron two times a week) and vitamin A (1,600 IU) once a week, and to purchase subsidized iodized salt (30 ppm) from the plantation ration shop. The average hemoglobin level of the pickers rose significantly (p < .001) from 11.0 g/dl at baseline to 11.9 g/dl at the end of the nine months of intervention. The average amount of tea per picker increased significantly (p < .001) from 22.9 to 25.6 kg. There was a significant decrease in the number of "moderate pickers," who picked between 14 and 25 kg per day, and a significant increase in the number of "good pickers," who picked more than 25 kg per day, and the earnings of the majority of the pickers increased. The management of the estate where the intervention occurred benefited from a decrease in the number of pickers needed during the supplement period from 2,857 to 2,763, with no significant change in the yield per hectare in the two years. The yield per hectare on the control estate was not significantly different from that on the intervention estate, and the average amount of tea picked per worker was the same for the two periods (20.8 and 20.7 kg).

A Multinutrient Package of Iron, Vitamin A, and Iodine Improved the Productivity and Earnings of Women Tea Pickers in South India

Tea-picking is a highly skilled activity that is usually performed by women. This study, conducted on the Balanoor Plantations, India, from 1996 to 1998, was successful in empowering 339 women pickers and their families to take iron (60 mg of elemental iron two times a week) and vitamin A (1,600 IU) once a week, and to purchase subsidized iodized salt (30 ppm) from the plantation ration shop. The average hemoglobin level of the pickers rose significantly (p &lt; .001) from 11.0 g/dl at baseline to 11.9 g/dl at the end of the nine months of intervention. The average amount of tea per picker increased significantly (p &lt; .001) from 22.9 to 25.6 kg. There was a significant decrease in the number of “moderate pickers,” who picked between 14 and 25 kg per day, and a significant increase in the number of “good pickers,” who picked more than 25 kg per day, and the earnings of the majority of the pickers increased. The management of the estate where the intervention occurred benefited from a decrease in the number of pickers needed during the supplement period from 2,857 to 2,763, with no significant change in the yield per hectare in the two years. The yield per hectare on the control estate was not significantly different from that on the intervention estate, and the average amount of tea picked per worker was the same for the two periods (20.8 and 20.7 kg).

Performance approximation of pick-to-belt orderpicking systems

In this paper, an approximation method is discussed for the analysis of pick-to-belt orderpicking systems. The aim of the approximation method is to provide an instrument for obtaining rapid insight in the performance of designs of pick-to-belt orderpicking systems. It can be used to evaluate the effects of changing the layout of the system, the number of picking stations, the number of pickers, the conveyor speed, the number of bins to be processed per day, the number of orderlines per bin, etc. Especially in the design phase, modeling and analysis speed is more important than accuracy. The method presented in this paper is based on Jackson network modeling and analysis. The method is fast and sufficiently accurate. The method is used by Ingenieursbureau Groenewout B.V., for early-stage evaluation of design alternatives of pick-to-belt orderpicking systems and general transportation systems.